#!/usr/bin/env python
# _*_ coding:utf-8 _*_


import tensorflow as tf
import numpy as np
# 使用numpy生辰假数据
x_data = np.float32(np.random.rand(2, 100))
y_data = np.dot([0.100, 0.200], x_data) + 0.300

# 构造一个线性模型
b = tf.Variable(tf.zeros([1]))
W = tf.Variable(tf.random_uniform([1, 2]), -1.0, 1.0)
y = tf.matmul(W, x_data) + b

# 最小化方差
loss = tf.reduce_mean(tf.square(y-y_data))
optimiser = tf.train.GradientDescentOptimizer(0.5)
train = optimiser.minimize(loss)

# 初始化变/量
init = tf.initialize_all_variables()



with tf.Session as sess:
    # 启动图
    sess.run(init)
    # 拟合平面
    for step in range(0, 201):
        sess.run(train)
        if step % 20 == 0:
            print(step, sess.run(W), sess.run(b))














